submitted by:
Jinish K
[attachment=8663]
ABSTRACT
The wireless body area network(WBAN) has emerged as a new technology forHealth care that allows the data of a patient’s vital body parameters and Movements tobe collected by small wearable or implantable sensors and Communicated using shortrange wireless communication techniques. Wireless body area networks(WBAN) hasshown great potential in improving health care quality,and thus has found a wide rangeof applications from Ubiquitous health monitoring and computer assisted rehabilitationto emergency Medical response systems.The purpose of this study is to introduce WBAN and Also give an understandingof what possibilities and challenges there are when Using short range wirelesscommunications in this domain.Here establish a prototype body Area network system using Bluetooth andchoose the electrocardiogram(E C G)Signals to test the data transmission performanceover this system.The wireless body area networks promise to revolutionize Health monitoring.since the sensors collect personal medical data,security and Privacy areimportant components in this kind of networks(WBAN).
INTRODUCTION
Our era is witnessing an increasing pressure on the quality and quantity ofhealthcare due to the increase of aging population, chronic diseases, and healthconsciousness of people. People put more attention in prevention and early riskdetection. A system that can continuously monitor the health condition of elderlypeople and share information with remote care providers or hospitals will be in greatdemand. As an effort of catching up with this trend, wireless body area network(WBAN) as an emerging technology for providing this kind of health information,has been attracting more and more attention recently. WBAN is still at its infancy andthere are a lot of open research problems.One of the emerging issues is how to exploitwireless communications technologies in WBANs. ZigBee is a low-cost low-powerstandard. The drawback of ZigBee is the limited data rate, which is 250 kbps whenoperating at 2.4 GHz band, in order to support heterogeneous medical services. Inaddition, it may suffer from strong interference radios as well as other devices suchas microwave ovens also operate in this frequency band. The backward compatibilityis another matter of concern. Ultra wide band (UWB) has also been considered asa potential wireless technology. However, the UWB wireless chips are not yet wellcommercialized at this moment.On the other hand, the Bluetooth is one of the most promising technologiesfor WBANs, particularly for wireless healthcare . The main advantages are the smallsize, reduced cost, low power consumption, and especially the great market penetration.The system employs a frequency-hopping multi-access scheme, which helps1combat interference and fading, and increase the security in radio transmission. Thedata rate is up to 1 Mbps , adequate for transmitting most real time biomedicalsignals. In addition, diverse prototypes of sensors based on Bluetooth have beendeveloped for different biomedical signals, including ECG, glucometers and evenstethoscopes. For instance, Continua Alliance has adopted Bluetooth as the wirelesslink for their wireless healthcare applications .These commercial Bluetooth terminalspermit a straightforward integration of general purpose devices (e.g., PDAs, smartphones,etc.) into WBAN. Despite the apparent suitability of Bluetooth technology for
medical WBANs,its performance in combination with medical data processing techniqueshas not been studied in the literature. here a case study on wireless ECG monitoringover Bluetooth page link in order to evaluate the data transmission quality and howit is affected by the ECG compression. Next propose a very low complexity ECG datacompression method and using this method, the influence of data compression on theECG signal reconstruction performance at the Bluetooth receiver will be studied[1].The deployment of WBAN for medical and non-medical Applications must satisfythe stringent security and privacy requirements. In case of medical applications,the security threats may lead a patient to a dangerous condition, and sometimes toa death. Thus a strict and scalable security mechanism is required to prevent maliciousinteraction with a WBAN. A secure WBAN should include confidentiality andprivacy, integrity and authentication, key establishment and trust setup, secure groupmanagement and data aggregation. However, the integration of a high level securitymechanism in a low power and resource-constraint sensor increases the computational,communication and management costs. In a WBAN, security and system performanceare equally important, and thus, designing a low power and secure WBAN system is afundamental challenge to the designers [2].
CHAPTER 2
NETWORK ARCHITECTURE OF MEDICAL WBAN
The WBAN architecture under consideration is shown in Figure 2.1. This
architecture consists of two main parts: multiple body sensor units and a body
central unit. The body sensor units are able to perform vital medical data acquisition,
data (pre-processing), actuator control, data transmission and some basic user
feedback. The body central unit links multiple sensor units, performs data collection,
data processing/compression, actuator control, basic event detection/management and
provides external access together with a personalized user interface. In this seminars
report, the intra-BAN communications between the body sensor units and the body
central unit is based on Bluetooth.
A PROTOTYPE BAN SYSTEM WITH BLUETOOTH
From a general understanding of the BAN and the system requirements, it is evident that possible candidates in implementing BAN should be short range commu¬nication technologies. Bluetooth operates in the 2.4GHz ISM band, from 2400MHz to2483.5MHz .The system employs a frequency-hopping multiple access schemes to combat interference and fading. The symbol rate is 1 M symbol/s supporting a bit rate of 1 Mb/s. For example, ECG signal from each channel are digitized at 360 Hz with 11-bit resolution implying a data rate of 3.84 Kbps per channel, so all 12 channels of ECG data can potentially be transmitted using Bluetooth. In addition, forward error correction (FEC) and automatic repeat request (ARQ) for retransmission are used as authentication of reception to ensure reliable communication. Based on its suitability of BAN, here test a prototype system for BAN using Bluetooth technology. Then will discuss the detailed system in the following [1].
3.1 A system block diagram
The whole system block diagram is in Figure 3.1. First, the digitized ECG signals are passed through the data compression module in order to reduce the trans¬mission requirement and the needed storage capacity. Then the compressed data are transmitted through the Bluetooth Radio System module. The details of these modules are described in the following sections. At the receiver, the inverse processes are performed to reconstruct the original signals[1].
3.2 ECG data compression for WBAN
By utilizing the ECG compression techniques, expect to achieve the objective of reducing the amount of digitized ECG data as much as possible while preserving the diagnostic information in the reconstructed signal. The compression ratio (CR) is a measure of the compression performance, defined as the ratio between the number of bits needed to represent the original and the compressed signals. For the error criterion, the percentage root-mean-square difference (PRD) measure is employed. However the clinical acceptability of the reconstructed signal should always be determined through visual inspection by physicians.
However, WBAN applications not only require small reconstruction error (distortion) and high compression ratio (CR), but also require these to come at low complexity. A compression method providing high CR with small distortion can reduce the cost of the wireless data transmission, and make it possible for prolonged local data storage at individual sensors until the detection of an emergency. However, existing ECG data compression approaches either do not achieve both high CR and small distortion, or provide these at very high complexity. On the other hand, low complexity is essential for wireless health monitoring sensors running on batteries, whose power efficiency and endurance can be life-critical. In this section, then propose a simple but highly effective ECG data compression method. Existing data compression techniques for ECG signals can be classified into three main categories:
1) direct data compression methods, 2) transformation methods, and 3) parameter extraction methods.
For the transformation methods, discrete cosine transform (DCT) and wavelet transforms have been widely investigated for lossy data compression. Here proposed method is a 2-stage data compression process that combines a lossy data compression technique and a lossless coding scheme. Both DCT and wavelet transforms have been widely investigated for lossy data compression. Here, the DCT-based transform is used in the first stage of the compression process due to the fact that the frequency of ECG signal concentrates mainly between 0.05 Hz and 130 Hz. Therefore, through DCT transform, we can represent the original ECG signal in a few transformed DCT coef¬ficients, which can achieve higher CR and is in sensitive to noise effect. In addition, the DCT-based method is simpler than wavelet based compression and more flexible to control the CR . After the DCT, the LZW coding is used in order to compress the DCT coefficients in the second stage of the compression process. LZW coding is a lossless dictionary based compression algorithm which looks for repetitive sequences of data and builds a dictionary based on them. Since it is a lossless compression, the percentage root-mean-square difference (PRD) can be well conserved. Therefore, the whole process of the ECG data compression can be summarized as follows:
1. Split the original signal into M blocks, each containing N samples;
2. Transform each block using DCT;
3. Retain K(< N) DCT coefficients;
4. Quantize the K retained DCT coefficients; and
5. Encode the quantized DCT coefficients using LZW coding.
To facilitate comparisons with existing approaches, the PRD is employed to measure the data reconstruction error:where xn is the original data and x~ is the reconstructed data after compression. This method uses a user specified PRD value to find the optimal threshold value for the DCT coefficients via an iterative method, which significantly increases the computational complexity. In general DCTLZW algorithm achieves low PRD and high CR at much lower complexity in comparison with existing alternatives. The low complexity is essential for wireless sensors running on batteries. This is particularly important for healthcare purpose sensors since their endurance can be life-critical. The low PRD and high CR are also important for WBAN. In particular, high CR reduces the cost/energy consumption of wireless transmissions. High CR further allows the sensors built-in memory card to store non-critical data, which can be collected upon the occurrence of an emergency. The high CR allows data storage of a long period pre- and post-emergency [1].
WIRELESS ECG MONITORING OVER BLUETOOTH
In this section, consider the ECG monitoring over the Bluetooth physical page link based on the proposed ECG compression method in the previous section. The overall system block diagram is shown in Fig. 3.1 First, the digitized ECG signals are passed through the data compression module. Then the compressed data is transmitted through the Bluetooth Radio System module. At the receiver, the reverse is performed to reconstruct the ECG signal.
4.1 Bluetooth Radio Link
Here first briefly describe the Bluetooth radio page link functions and parameters.
4.1.1 Bluetooth Transmitter
The block diagram of the Bluetooth transmitter is shown in Fig. 4.1. The transmitter uses Gaussian Frequency Shift Keying (GFSK) modulation. A pass band transmitted GFSK signal can be represented as
4.1.2 Bluetooth Receiver
At the receiver,use a simple phase differential demodulator. From the GFSK modulator, we know that bit '1'results in a positive slope in phase and bit '0' results in a negative slope. The phase is extracted by passing the In-phase and Quadrature path of the complex base-band signal to an arc tan block. Then the resulted phase is sampled at T intervals. Denote the phase difference of the nth and (n-1)th samples as A0n . Then the nth transmitted symbol is determined as '1' if A0n is positive and vice versa.
4.2 Simulation
The MIT-BIH Arrhythmia database was used to evaluate the proposed data compression and modulation schemes. In this standard database, the ECG signals were digitized through sampling at 360 Hz with 11-bit resolution. The first 10000 samples of 10 MIT-BIH records have been tested.
4.2.1 ECG data compression performance
For the data record 100, 101, 102, 103, 104, 105, 106, 107, 108, and 109, table 1 gives the simulation results on CR and PRD. As shown in the table, here can achieve a CR of 6:1 to 14.5:1 with the PRD of about 5. The distortion is mainly due to the quantization process in the compression. With the optimization for the quantization and expect to reduce the distortion and achieve a PRD
4.2.2 bit error rate performance
Figure 4.4 shows the plot of BER vs. SNR in the presence of additive white Gaussian noise (AWGN) and the effect of the multipath channel. As we can see in the figures, it takes more than 30 dB SNR to achieve an acceptable BER of the order of 10 3 in the fading channel. This can be potentially improved by designing more optimal and sophisticated receiver schemes.
4.2.3 Overall system performance
Figures 4.5 and 4.6 show the overall system performance with SNR equals to 29dB and 30dB, respectively. Plot (a) in both figures is a segment of the original ECG signal and plot (b) is a segment of reconstructed ECG signal. As we can see, when SNR equals 29dB, the reconstructed signal exhibits severe distortion. When SNR equals 30dB, the calculated PRD is about 95, but the reconstructed signal seems to retain the basic shape and clinical features of the original signal in this case. We can take a close look at one period of the ECG waveform as shown in Figure 4.7, the PRD is relatively high because there are many subtle differences between the original and reconstructed signal, which does not seem to influence the peaks of the general waveform. Therefore, in order to keep the fidelity of the original ECG signal, it appears that the signal to noise ratio must be at least 30 dB, which is fairly high. This will increase the emission power and power consumption and not feasible to BAN with ultra-low power requirement for BAN. A possible solution to this problem is to design more sophisticated demodulation schemes for GFSK modulation in fading channels
ULTRA LOW POWER SENSOR DESIGN FOR WBAN: CHALLENGES, POTENTIAL SOLUTIONS,
AND APPLICATIONS
A typical sensor node in WBAN should ensure the accurate sensing of the signal from the body, carry out low level processing of the sensed signal, and wireless transmit the processed signal to a local processing unit. The main challenges for successful realization of the sensor nodes can be summarized as follows:
1. The overall size and weight of sensor nodes should be tailored to the human body. It is expected that the sensor nodes could become invisible in order to avoid activity restriction or behavior modification. This requires new integration and pack¬aging technologies.
2. The total energy consumption of sensor needs to be drastically reduced to allow energy autonomy. This is especially important for implantable sensors. As the energy autonomy of current battery-powered sensors is limited, the energy harvesting tech¬nology could be integrated to significantly extend the operating life of sensor nodes.
3. The security of WBAN should be guaranteed to protect the patient's privacy. The sensed signal from the body should have secure and limited access. It should be very important that the sensed signal from one person cannot be mixed up with that from another person.
4. The reliability needs to be paid special attention. An undetected life critical signal could lead to fatal consequences. The improvement of reliability requires minimizing not only wireless communication errors but also sensing and read-out errors.
5. The intelligence should be added to sensors so that each one is capable of storing,
6. processing and transferring signal continuously or on an event-triggered basis. Intelli¬gence could also be introduced at the network level to deal with issues such as network management, data integration and data interpretation.
7. To address the above challenges, the successful design of WBAN requires expertise in wireless communication, digital signal processing, sensing and read-out, energy harvesting, and packaging and integration. Advances in the above key areas facilitates the rapid development of WBAN [3] .
8. 5.1 Energy Harvesting
9. One of the key considerations in WBAN design is the energy supply of sensor nodes. The size of the energy supply increases with the required store energy and is typically the largest contributor to the size of the sensor node. Thus, the overall power consumption of the sensor nodes is expected to achieve below 1 mW . This expected power demand is sufficiently low such that the harvested energy technology could start to be integrated in the sensor node to partly or fully replace the battery.
10. Energy harvesting is a relatively young research field and has been developed for about 10 years. It takes the energy - mechanical, thermal, or light - from the ambient environment and converts this into electrical energy, which is stored in an energy storage system (ESS). The energy storage system balances the energy gener¬ation and consumption, subsequently, if the mean generated energy is at least equal to the mean consumed energy, there is no more need to replace or externally recharge and monitor the energy storage system during the complete operational lifetime of the device.
11. 5.1.1 Energy Source
12. A) Harvesting Energy from Motion and Vibration
For converting motion or vibration, the principle of inertia has to be used: the trans¬ducer is inserted in a frame, one part of it is fixed to the frame itself, and the other can move. The frame is attached to the moving or vibrating object and relative motion of the parts of the transducer is controlled by the law of inertia (see Figure). This approach is the most widely used for harvesting energy from vibration;in most cases the system is made resonant by means of suspending the moveable part to a spring.
B) Harvesting Energy from Temperature Differences
Thermal energy harvesters are based on the Seebeck effect: when two junctions, made of two dissimilar conductors, are kept at a different temperature an open circuit voltage develops between them.
C) Photovoltaic Harvesting
Photovoltaic (PV) converts incoming photons into electricity. Outdoor these cells have been used for many years, where power densities are available upto 100 mW/cm2. Efficiencies range from 5% to 30%, depending on the material. Indoor the situation is much different, since the illumination levels are much lower than outdoor (100 to 1000 /lW/cm2). Furthermore, at low illumination levels, the efficiency of solar cells will drop considerably. Much research is therefore needed to optimize these cells for low level illuminations.
D) RF Energy Harvesting
Ambient radio frequency (RF) energy, which is available through public telecommu¬nication services e.g. global system for mobile communications (GSM), wireless local area network (WLAN), is also a possible source for energy harvesting. When harvesting energy in the GSM or WLAN band, one has to deal with very low power density levels. For distances ranging from 25 m to 100 m from a GSM base station, power density levels that ranges only from 0.1 mW/m2 to 1.0 mW/m2 may be expected
. For WLAN environments, power density levels that are at least one order of magnitude lower are found . Therefore, neither GSM nor WLAN are likely to produce enough ambient RF energy for wirelessly powering miniature sensors, unless a large area is used for harvesting. Alternatively, the total antenna surface can be minimized if one uses a dedicated RF source, which can be positioned close (a few meters) to the sensor node, thereby limiting the transmission power to levels accepted by interna¬tional regulations[3].
5.2 Wireless Communication
The name of WBAN clearly indicates that wireless communication is one of the most important aspects in the design of WBAN. The presence of human body poses many new wireless communication challenges.
5.2.1 Propagation Environments
In general, the human body is not a friendly environment for wireless commu¬nication. It is partially conductive and consists of materials of different dielectric constants, thickness, and characteristic impedance. Therefore, the human body can significantly influence the behavior of propagation and lead to high losses. Furthermore, the movement of the body when combined with wave obstruction can lead to significant signal fluctuations.
Thus, a good understanding of the characteristics of propagation environments on, in, or around the body is critical to the design of wireless communication for WBAN. However, for propagation inside the human body, physical measurement and experimental study is hardly to be feasible. One alternative in the current stage is to use a three-dimensional (3D) simulation and visualization scheme . Simulated results in this study have shown that the path loss attenuates much faster with longer distance compared with a free-space transmission scenario as expected. However, as the human body is too complicated and composed of varied components that are non-predictable and will change with a person's age, weight, postures, etc., further vali¬dated results are expected in the future.For propagation on the surface of the human body, or propagation from the surface of the human body to the external device, the physical measurement and experimental study is easier to be performed.
5.2.2 Power Consumption
The wireless communication is often a major power consumer in the sensor node of WBAN. typical commercial chipsets consume in the order of 10 to 100 mW for data rates of 200 to 2000 kbps, leading to a power efficiency of roughly 50 to 400 nJ/bit. The Nordic nRF24L01 achieves less than 20 nJ/bit, but to the expense of a limited set of functionalities. Consider the chipset with the lowest achievable transmission power of 10 mW, a typical battery with a capacity of 1250 mAh and a voltage of 1.5 V could only continuously supply it for around 1 week. The need for replacing or recharging batteries in such a high frequency is normally undesirable for wearable WBAN and unacceptable for implantable WBAN. Thus, the current available wireless technology is already a major bottleneck to impede the further development of WBAN whose expected overall power budget is below 1 mW . For this reason, new ultra low power wireless technologies are required, which could consume one to two orders of magnitude less than today's wireless technologies In IMEC/Holst Centre, the target is to reach an energy efficiency of 1 nJ/bit, which, at a nominal rate of 200 kbps, translates into an average power consumption of 200 /lW. Complemented with novel network and protocol schemes, these ultra-low-power transceivers have the potential to virtually eliminate standby power while still providing the robustness and reliability required for WBAN applications.
One of the widely known solutions to achieve low power consumption is the duty cycling, which means that the signal is transmitted only at a fraction of total transmission time. The duty cycling allows switching on the radio front-ends only for the instants where signals must be transmitted or received, and could thus significantly reduce the average power consumption. In principle, the duty cycling requires that the bandwidth should be larger than the symbol rate. The larger ratio of the bandwidth over the symbol rate could result in the more significant power consumption reduction. Thus, the impulse radio (IR) based ultra-wideband (IR-UWB) is one of the suitable choices. The first full integration of a carrier based IR-UWB transmitter in a standard logic 180 nm complementary metal-oxide-semiconductor (CMOS) technology[3].
5.3 Digital Signal Processing
As shown in the previous section, wireless communication is a significant power consuming component in the sensor node of WBAN. Typically, the reduction of data rate could result in the reduction of power consumption of wireless communi¬cation. For this reason, the sensor node should be equipped with sufficient intelligence and processing capabilities to extract important features of the raw data sensed, and thus to minimize the amount of data being transferred through wireless communi¬cation. studies showes that for ECG signal monitoring, using ECG delineation algo¬rithm to process the data locally, reduces the data transmission rate and hence the power consumed by the wireless communication by more than 50%. However, this on-node processing will consume additional power to extract and compress information, and thus create a tradeoff between signal processing and wireless communication. To get a positive net power saving, the on-node processing must be done efficiently. For this reason, one of research directions is to design application specific instruction-set processors (ASIP) for body area network applications. We have recently developed an ASIP that consumes 200 /lW tailored for processing 24-channel EEG signal processing in 90 nm technology. Another research direction is to facilitate low power operation of digital part of wireless communication, e.g. MAC protocols and baseband algorithms, by using power management to efficiently control the power distribution of the processors[3].
5.4 Sensing and Read-Out
The sensing and read-out of the signals may draw a significant part of the power budget in today's sensor nodes in WBAN, especially when the number of signals or channels is increasing. Thus, reducing the power required for signal extraction is an important challenge here. In addition, the acquisition of bio-potential signals, namely
EEG, ECG, EMG and EOG signals, presents an interesting challenge as the signal amplitudes are in the //V range. Various noise sources, such as electrode offset voltage and interference from power-lines, requires high-performance readout circuit design that is capable of rejecting such aggressors while amplifying the weak bio potential signals. Addressing the above challenges, a family of ultra-low-power front-ends for the read-out of bio-potential signals has been developed . The key achievements in these ASICs are their high performance with ultra-low power dissipation. The prior leads to the extraction of clean biopotential signals while the latter ensures the compat¬ibility with battery operated systems. In addition, an important feature of these readout circuits is their programmable gain and filter characteristics enabling their use for different applications that may require the monitoring of different biopotential signals.
Most recently, the design of a complete low-power EEG acquisition ASIC targeted to miniaturized ambulatory EEG acquisition systems . The ASIC consists of eight readout channels, an 11-bit ADC, a square-wave oscillator and a bias circuit. In addition to the acquisition mode, the ASIC has calibration and electrode impedance measurement modes. The prior enables the remote testing of circuit operation, where as the latter is useful for remote assessment of biopotential electrode quality. Both of these features are important in terms of the reliability of sensor nodes in WBAN [3].
CONCLUSION
This report carried out a case study on wireless ECG data transmission using Bluetooth technology. To facilitate such a study, first proposed a low complexity ECG compression algorithm by combining DCT and LZW. Although both DCT and LZW are existing techniques, the combination is new. And when compared with existing alternatives, the proposed method gives superb compression performance with very low complexity and high flexibility. These make it very suitable for WBAN applica¬tions with low power and high performance requirements. Then investigated the ECG signal reconstruction performance over a wireless Bluetooth page link in fading channels with AWGN. Through this study,found that directly transmitting the raw ECG data is not advantageous compared to the transmission of compressed data. In addition, there exists an optimum ECG compression ratio for the wireless link.
Then overviewed different technologies in the field of energy harvesting, wireless body area networks with the focus on wireless communication, digital signal processing, and sensing and read-out circuits. With the increasing improvement of miniaturization, cost and power consumption of the wireless sensor nodes, we can expect that the autonomous, unobstructed, pervasive, and invisible wireless body sensor network could be commercially realized in the future.
